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Author |
Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
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Title |
Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology |
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Conference Article |
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Year |
2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
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10255 |
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287-294 |
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Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model |
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Abstract |
Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach. |
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Faro; Portugal; June 2017 |
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L.A. Alexandre; J.Salvador Sanchez; Joao M. F. Rodriguez |
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978-3-319-58837-7 |
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IbPRIA |
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DAG; 602.006; 600.097; 600.121 |
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Admin @ si @ RFV2017 |
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2952 |
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Author |
Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva |
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Title |
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering |
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Conference Article |
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2017 |
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8th Iberian Conference on Pattern Recognition and Image Analysis |
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Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks |
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In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed. |
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Faro; Portugal; June 2017 |
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MILAB; no proj |
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Admin @ si @ BPC2017 |
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2939 |
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Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |
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Title |
Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs |
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Conference Article |
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2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
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Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines |
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We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction. |
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Faro; Portugal; June 2017 |
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DAG; 600.097; 600.121 |
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Admin @ si @ JRL2017a |
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2953 |
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